Many attempts have been made over the years to improve the way users interact with computers. In the beginning, cards or tapes with punched holes were used for user input. Punch cards gave way to terminals with alphanumeric keyboards and text displays, which evolved into the modern keyboard, mouse, and graphical-display based graphical user interfaces. Many expect that the use of multi-finger, touch-sensitive user interfaces (“multi-touch interfaces”, such as those described in the references incorporated above, will become widely adopted for interacting with computers and other electronic devices, allowing computer input to become even more straightforward and intuitive.
Users of these multi-touch interfaces may make use of hand and finger gestures to interact with their computers in ways that a conventional mouse and keyboard cannot easily achieve. A multi-touch gesture can be as simple as using one or two fingers to trace out a particular trajectory or pattern, or as intricate as using all the fingers of both hands in a complex sequence of movements reminiscent of American Sign Language. Each motion of hands and fingers, whether complex or not, conveys a specific meaning or action that is acted upon by the computer or electronic device at the behest of the user. The number of multi-touch gestures can be quite large because of the wide range of possible motions by fingers and hands. It is conceivable that an entirely new gesture language might evolve that would allow users to convey complex meaning and commands to computers and electronic devices by moving their hands and fingers in particular patterns.
Techniques for teaching these gestures and gesture languages to new users are needed. Techniques that have been proposed include playback of motion trails indicating the gesture, animated hands performing the gesture, and various graphical depictions of gestures and their meanings. Each of these techniques suffers from one or more deficiencies.
For example, a motion trail 100 corresponding to a thumb and two-finger rotate gesture is illustrated in FIG. 1. The paths 101, 102, and 103 can correspond to the thumb, index, and middle finger motions, with the arrowheads indicating the direction of motion. The paths may be color-coded or have other indication of what finger(s) are used. For example, hand icon 104 can be provided and can include dots to indicate what fingers are used. Even if the motion trails are animated, the abstraction level of this teaching technique can still lead to difficulty in comprehension for a user. Additionally, this technique lacks interactivity and feedback. Therefore, this technique does not lend itself to teaching and practice of a particular gesture or group of gestures by a user.
An animated hand gesture (again illustrating a thumb and two finger clockwise rotation gesture) is illustrated in FIG. 2. Animated hand gestures may range in complexity from relatively simple line drawings to complex three-dimensional renderings of the hand. These animated hand gestures can address the abstraction problem to a degree by providing a more tangible representation of the users hand 200. However, this technique may not clearly indicate the path taken by the various hand parts. For example, paths may be obscured by the hand representation. Additionally, this technique also lacks interactivity and feedback that may be beneficial for teaching and practice.
The gesture dictionaries disclosed in U.S. patent application Ser. Nos. 11/619,571 and 11/619,553, each titled “Multi-Touch Gesture Dictionary,” and each filed Jan. 3, 2007 (referenced above) can be adapted as described therein to provide a degree of interactivity. For example, a graphical depiction of a gesture performed may be highlighted or otherwise indicated. Additionally, a simple form of feedback arises from the fact that a gesture that is incorrectly executed will cause the wrong command to be displayed. However, this relatively simple level of interactivity and feedback may be better suited for teaching additional gesture “vocabulary” to users that are already somewhat acquainted with some gestures. Gesture learning from ground zero may be enhanced by techniques that incorporate more robust interactivity and feedback.